Who does L&Y Decision work with?

L&Y Decision works with e-commerce brands, DTC companies, and retail businesses, typically with revenues between $500K and $50M, who are serious about making evidence-based marketing decisions. They specialise in small and medium businesses ready to graduate from gut feeling.

What data access does L&Y Decision need to get started?

Read-only access to ad platforms (Meta Ads, Google Ads), analytics (GA4 or equivalent), and commerce data (Shopify, WooCommerce, etc.). L&Y Decision never touches campaign settings, budgets, or creative.

How quickly will e-commerce brands see results from decision science?

Most engagements deliver initial insights and recommendations within 2–4 weeks. Implementation of those recommendations typically produces measurable impact within 60–90 days. Causal inference and attribution model projects take 4–8 weeks by design.

Do you need an in-house data team for marketing attribution modeling?

No. L&Y Decision handles data extraction, transformation, and modeling entirely. If you can grant read access to your platforms, they do the rest. Many clients have no dedicated data function at all.

What is causal inference in marketing?

Causal inference in marketing is a statistical method that isolates the true incremental impact of advertising: measuring what would have happened without the ads (the counterfactual). Unlike correlation-based attribution, causal methods such as geo-holdout experiments, difference-in-differences, and Bayesian structural time-series prove that ads caused revenue, not merely correlated with it.

What is marketing mix modeling (MMM)?

Marketing mix modeling (MMM) is a statistical technique that quantifies how each marketing channel, paid search, social, email, TV, out-of-home, contributes to sales. Unlike last-click attribution, MMM uses aggregate time-series data to measure true channel contribution, accounting for external factors like seasonality and price changes.

How do CMOs and founders justify marketing analytics investment to their CFO or board?

L&Y Decision builds board-ready marketing ROI models that CFOs and investors can audit. Platform-reported attribution overclaims by 30–200% due to cross-channel double-counting. Causal models, using geo-holdout experiments and difference-in-differences analysis, isolate true incremental revenue. Clients typically discover they can reallocate 20–35% of ad spend to higher-performing channels, reducing customer acquisition cost by 25–35% on average.

Is L&Y Decision suitable for small and medium-sized businesses or small-cap public companies?

Yes. L&Y Decision was built specifically for brands that want enterprise-grade measurement without an in-house data science team. Typical clients are e-commerce, DTC, or retail brands with revenues between $500K and $50M, spending $10K–$100K per month on paid advertising. L&Y Decision handles all data extraction, modeling, and interpretation. No data engineering resources required.

Free tool · Experimentation

A/B Test Significance Calculator

A green 'winner' badge in your testing tool is the start of the question, not the answer. Enter sessions and orders for each version to see the p-value, the plausible range of the lift, and whether the test had enough traffic to detect a lift that matters.

Result

Significant

p-value

0.0232

Relative lift

+15.00%

Control CVR

2.4%

Variant CVR

2.76%

95% interval for the difference: +0.05 to +0.67 pts. This test could reliably detect lifts of about 17.9% or more. Checked it early?

Talk to us about your measurement

Worked example

Control converts 480 of 20,000 sessions (2.4%); the variant converts 552 of 20,000 (2.76%). That is a 15% relative lift with p ≈ 0.023, significant at 95%, with a 95% interval of about +0.05 to +0.67 percentage points.

Read the interval, not just the p-value

The calculator runs a two-sided two-proportion z-test, the standard test behind most A/B tools. The p-value tells you how surprising the gap would be if the variant did nothing. The confidence interval tells you what you actually need for a business case: the range of lifts consistent with the data.

Plan on the low end of that interval. Winners picked because they crossed a significance line overstate their effect on average, so the lift you see after rollout is usually smaller than the lift in the test.

A non-significant test is not proof of no effect

The calculator also shows the smallest relative lift your sample could reliably detect. If that number is 25% and you were hoping for 8%, the test never had a chance. You learned that the change is not enormous, nothing more. Size the next test before launch with the sample size calculator.

Frequently asked questions

What confidence level should I use?

95% is the standard. Use 99% when a false winner would trigger a large, hard-to-reverse investment such as a full site redesign or a pricing change.

Can I test revenue per session with this?

No. This calculator handles conversion rates. Revenue per session is skewed by a small number of large orders and needs a t-test or bootstrap on per-session revenue.

Why did my winner not hold up after launch?

Usually some mix of peeking, testing many variants or metrics and reporting the best, and the natural overstatement of effects selected for significance. Fix the sample in advance and read the result once.

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